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max_epochs

max_epochs: <int> (Optional)

Description

The max_epochs parameter specifies the maximum number of epochs to train your model. An epoch is defined as the process of completing a specific number of training steps. At the end of each epoch, Kumo evaluates the model on the validation set to track its performance.

Kumo organizes the training and evaluation process using max_epochs alongside other key parameters, including num_experiments, max_steps_per_epoch, max_val_steps, and max_test_steps.

Below is the pseudo code that demonstrates how Kumo manages the training and evaluation process:

for experiment in range(num_experiments):
  model = create_new_model()

  for epoch in range(max_epochs):

    # Training
    for training_step in range(max_steps_per_epoch):
      batch = sample_mini_batch(batch_size, "train")
      pred = model(batch)
      loss = compute_loss(pred, batch)
      train_metrics = compute_metrics(pred, batch)
      loss.backward()
      optimizer.step()

    # Validation
    for validation_step in range(max_val_steps):
      batch = sample_mini_batch(batch_size, "val")
      pred = model(batch)
      val_metrics = compute_metrics(pred, batch)
      
    if should_early_stop:
      break

# Testing
for test_step in range(max_test_steps):
  batch = sample_mini_batch(batch_size, "test")
  pred = best_model(batch)
  test_metrics = compute_metrics(pred, batch)

Supported Task Types

  • All

Default Values

run_modeDefault Value
FAST8
NORMAL12
BEST16